论文标题
安全：相似性 - 意识多模式的假新闻检测
SAFE: Similarity-Aware Multi-Modal Fake News Detection
论文作者
论文摘要
有效检测假新闻最近引起了极大的关注。当前的研究为预测假新闻做出了重大贡献，而较少专注于新闻文章中文本和视觉信息之间的关系（相似性）。将重要性附加到这种相似性上有助于确定假新闻故事，例如，这些故事试图使用无关的图像来吸引读者的注意力。在这项工作中，我们提出了一个$ \ mathsf {s} $ imurality-$ \ m athsf {a} $ ware $ \ mathsf {f} $ ak $ \ ak $ \ mathsf {e} $新闻检测方法（$ \ mathsf {safe} $），调查了多人（textual and textual and Vissional and Visual）的信息。首先，采用神经网络来分别提取新闻表示的文本和视觉特征。我们进一步研究了跨模态提取的特征之间的关系。新闻文本和视觉信息及其关系的这种表示是共同学习的，并用于预测假新闻。提出的方法促进了基于其文本，图像或“不匹配”的新闻文章的虚假性。我们对大型现实世界数据进行了广泛的实验，这些实验证明了该方法的有效性。
Effective detection of fake news has recently attracted significant attention. Current studies have made significant contributions to predicting fake news with less focus on exploiting the relationship (similarity) between the textual and visual information in news articles. Attaching importance to such similarity helps identify fake news stories that, for example, attempt to use irrelevant images to attract readers' attention. In this work, we propose a $\mathsf{S}$imilarity-$\mathsf{A}$ware $\mathsf{F}$ak$\mathsf{E}$ news detection method ($\mathsf{SAFE}$) which investigates multi-modal (textual and visual) information of news articles. First, neural networks are adopted to separately extract textual and visual features for news representation. We further investigate the relationship between the extracted features across modalities. Such representations of news textual and visual information along with their relationship are jointly learned and used to predict fake news. The proposed method facilitates recognizing the falsity of news articles based on their text, images, or their "mismatches." We conduct extensive experiments on large-scale real-world data, which demonstrate the effectiveness of the proposed method.
